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IPF-HMGNN: A novel integrative prediction framework for metro passenger flow

Wenbo Lu, Yong Zhang, Hai L. Vu, Jinhua Xu, Peikun Li

TL;DR

A novel Integrative Prediction Framework with the Hierarchical Message-Passing Graph Neural Network (IPF-HMGNN) is proposed, which can significantly reduce the mean absolute error (MAE) and root mean square error (RMSE) of the GNN prediction model.

Abstract

The operation and management of the metro system in urban areas rely on accurate predictions of future passenger flow. While using all the available information can potentially improve on the accuracy of the flow prediction, there has been little attention to the hierarchical relationship between the type of tickets collected from the passengers entering/exiting a station and its resulting passenger flow. To this end, we propose a novel Integrative Prediction Framework with the Hierarchical Message-Passing Graph Neural Network (IPF-HMGNN). The proposed framework consists of three components: initial prediction, task judgment and hierarchical coordination modules. Using the Wuxi, China metro network as an example, we study two prediction approaches (i) traditional prediction approach where the model directly predicts passenger flow at the station, and (ii) hierarchical prediction approach where the prediction of ticket type and station passenger flow are performed simultaneously considering the hierarchical constraints (i.e., the sum of predicted passenger flow per ticket type equals the predicted station aggregated passenger flow). Experimental results indicate that in the traditional prediction approach, our IPF-HMGNN can significantly reduce the mean absolute error (MAE) and root mean square error (RMSE) of the GNN prediction model by 49.56% and 53.88%, respectively. In the hierarchical prediction approach, IPF-HMGNN can achieve a maximum reduction of 35.32% in MAE and 36.18% in RMSE, while satisfying the hierarchical constraint.

IPF-HMGNN: A novel integrative prediction framework for metro passenger flow

TL;DR

A novel Integrative Prediction Framework with the Hierarchical Message-Passing Graph Neural Network (IPF-HMGNN) is proposed, which can significantly reduce the mean absolute error (MAE) and root mean square error (RMSE) of the GNN prediction model.

Abstract

The operation and management of the metro system in urban areas rely on accurate predictions of future passenger flow. While using all the available information can potentially improve on the accuracy of the flow prediction, there has been little attention to the hierarchical relationship between the type of tickets collected from the passengers entering/exiting a station and its resulting passenger flow. To this end, we propose a novel Integrative Prediction Framework with the Hierarchical Message-Passing Graph Neural Network (IPF-HMGNN). The proposed framework consists of three components: initial prediction, task judgment and hierarchical coordination modules. Using the Wuxi, China metro network as an example, we study two prediction approaches (i) traditional prediction approach where the model directly predicts passenger flow at the station, and (ii) hierarchical prediction approach where the prediction of ticket type and station passenger flow are performed simultaneously considering the hierarchical constraints (i.e., the sum of predicted passenger flow per ticket type equals the predicted station aggregated passenger flow). Experimental results indicate that in the traditional prediction approach, our IPF-HMGNN can significantly reduce the mean absolute error (MAE) and root mean square error (RMSE) of the GNN prediction model by 49.56% and 53.88%, respectively. In the hierarchical prediction approach, IPF-HMGNN can achieve a maximum reduction of 35.32% in MAE and 36.18% in RMSE, while satisfying the hierarchical constraint.
Paper Structure (28 sections, 16 equations, 24 figures, 9 tables, 1 algorithm)

This paper contains 28 sections, 16 equations, 24 figures, 9 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Decomposition and aggregation modeling
  4. Deep learning methods based on GNN model
  5. Preliminaries
  6. Problem formulation
  7. Message Passing in GNN
  8. Hierarchical prediction problem
  9. Methodology
  10. IPF-HMGNN Framework
  11. Construct Hierarchical graph
  12. Input Patching Construct
  13. Multi-level Message Passing
  14. Hierarchical Coordination
  15. Model learning
  16. ...and 13 more sections

Figures (24)

  • Figure 1: An example of a multi-layer graph.
  • Figure 2: The hierarchical structure existing in the metro passenger flow sequence.
  • Figure 3: The overview of IPF-HMGNN framework; (a) the process for getting the initial output; (b) the task judgment module; (c) the process of prediction coordination. Note: The nodes in the input graph depend on the prediction task, which may be stations or ticket types; "Output in prediction layer" means that we only output the results of those nodes in the prediction layer. For example, we do not output the results of the middle layer (e.g. clustering layer) nodes. Specific examples can be found in Section \ref{['5.3']}.
  • Figure 4: The process of constructing the bottom graph.
  • Figure 5: The process of constructing a hierarchical graph.
  • ...and 19 more figures